{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "584802a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "        \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn import preprocessing\n",
    "import xgboost as xgb\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "sns.set_style('whitegrid')\n",
    "from sklearn.model_selection import train_test_split\n",
    "#https://www.kaggle.com/datasets/neuromusic/avocado-prices\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6235950c",
   "metadata": {},
   "source": [
    "##githun url: https://github.com/carsonxie/Machine-Learning-Projects/tree/main/c1%20Supervised%20Learning%20method\n",
    "\n",
    "## Outline:\n",
    "### 1. Load and clean data: \n",
    "the data is clean, just need to convet Data to date type and use label encoder convert type.\n",
    "### 2. EDA\n",
    "### 3. Regression: predict price as respond variable.\n",
    "lasso regression, linear regression and random forest regressor.\n",
    "### 4. Classification on type: conventional or organic\n",
    "Logistic regression and random forest.\n",
    "### 5. Conclusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "d530a6fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Df shape :  (18249, 14)\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(\"avocado.csv\")\n",
    "print(\"Df shape : \", df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "aa2e18ac",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>AveragePrice</th>\n",
       "      <th>Total Volume</th>\n",
       "      <th>4046</th>\n",
       "      <th>4225</th>\n",
       "      <th>4770</th>\n",
       "      <th>Total Bags</th>\n",
       "      <th>Small Bags</th>\n",
       "      <th>Large Bags</th>\n",
       "      <th>XLarge Bags</th>\n",
       "      <th>type</th>\n",
       "      <th>year</th>\n",
       "      <th>region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-12-27</td>\n",
       "      <td>1.33</td>\n",
       "      <td>64236.62</td>\n",
       "      <td>1036.74</td>\n",
       "      <td>54454.85</td>\n",
       "      <td>48.16</td>\n",
       "      <td>8696.87</td>\n",
       "      <td>8603.62</td>\n",
       "      <td>93.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>conventional</td>\n",
       "      <td>2015</td>\n",
       "      <td>Albany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-12-20</td>\n",
       "      <td>1.35</td>\n",
       "      <td>54876.98</td>\n",
       "      <td>674.28</td>\n",
       "      <td>44638.81</td>\n",
       "      <td>58.33</td>\n",
       "      <td>9505.56</td>\n",
       "      <td>9408.07</td>\n",
       "      <td>97.49</td>\n",
       "      <td>0.0</td>\n",
       "      <td>conventional</td>\n",
       "      <td>2015</td>\n",
       "      <td>Albany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-12-13</td>\n",
       "      <td>0.93</td>\n",
       "      <td>118220.22</td>\n",
       "      <td>794.70</td>\n",
       "      <td>109149.67</td>\n",
       "      <td>130.50</td>\n",
       "      <td>8145.35</td>\n",
       "      <td>8042.21</td>\n",
       "      <td>103.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>conventional</td>\n",
       "      <td>2015</td>\n",
       "      <td>Albany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-12-06</td>\n",
       "      <td>1.08</td>\n",
       "      <td>78992.15</td>\n",
       "      <td>1132.00</td>\n",
       "      <td>71976.41</td>\n",
       "      <td>72.58</td>\n",
       "      <td>5811.16</td>\n",
       "      <td>5677.40</td>\n",
       "      <td>133.76</td>\n",
       "      <td>0.0</td>\n",
       "      <td>conventional</td>\n",
       "      <td>2015</td>\n",
       "      <td>Albany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-11-29</td>\n",
       "      <td>1.28</td>\n",
       "      <td>51039.60</td>\n",
       "      <td>941.48</td>\n",
       "      <td>43838.39</td>\n",
       "      <td>75.78</td>\n",
       "      <td>6183.95</td>\n",
       "      <td>5986.26</td>\n",
       "      <td>197.69</td>\n",
       "      <td>0.0</td>\n",
       "      <td>conventional</td>\n",
       "      <td>2015</td>\n",
       "      <td>Albany</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  AveragePrice  Total Volume     4046       4225    4770  \\\n",
       "0  2015-12-27          1.33      64236.62  1036.74   54454.85   48.16   \n",
       "1  2015-12-20          1.35      54876.98   674.28   44638.81   58.33   \n",
       "2  2015-12-13          0.93     118220.22   794.70  109149.67  130.50   \n",
       "3  2015-12-06          1.08      78992.15  1132.00   71976.41   72.58   \n",
       "4  2015-11-29          1.28      51039.60   941.48   43838.39   75.78   \n",
       "\n",
       "   Total Bags  Small Bags  Large Bags  XLarge Bags          type  year  region  \n",
       "0     8696.87     8603.62       93.25          0.0  conventional  2015  Albany  \n",
       "1     9505.56     9408.07       97.49          0.0  conventional  2015  Albany  \n",
       "2     8145.35     8042.21      103.14          0.0  conventional  2015  Albany  \n",
       "3     5811.16     5677.40      133.76          0.0  conventional  2015  Albany  \n",
       "4     6183.95     5986.26      197.69          0.0  conventional  2015  Albany  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(['Unnamed: 0'],axis=1,inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "399f79b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 18249 entries, 0 to 18248\n",
      "Data columns (total 13 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   Date          18249 non-null  object \n",
      " 1   AveragePrice  18249 non-null  float64\n",
      " 2   Total Volume  18249 non-null  float64\n",
      " 3   4046          18249 non-null  float64\n",
      " 4   4225          18249 non-null  float64\n",
      " 5   4770          18249 non-null  float64\n",
      " 6   Total Bags    18249 non-null  float64\n",
      " 7   Small Bags    18249 non-null  float64\n",
      " 8   Large Bags    18249 non-null  float64\n",
      " 9   XLarge Bags   18249 non-null  float64\n",
      " 10  type          18249 non-null  object \n",
      " 11  year          18249 non-null  int64  \n",
      " 12  region        18249 non-null  object \n",
      "dtypes: float64(9), int64(1), object(3)\n",
      "memory usage: 1.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "b14ed973",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "conventional    9126\n",
      "organic         9123\n",
      "Name: type, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(df['type'].value_counts())\n",
    "sns.countplot(x='type', data=df, palette='Set2')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "ed575a32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>day of week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>27</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015</td>\n",
       "      <td>11</td>\n",
       "      <td>29</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18244</th>\n",
       "      <td>2018</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18245</th>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18246</th>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18247</th>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18248</th>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18249 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       year  month  day  day of week\n",
       "0      2015     12   27            6\n",
       "1      2015     12   20            6\n",
       "2      2015     12   13            6\n",
       "3      2015     12    6            6\n",
       "4      2015     11   29            6\n",
       "...     ...    ...  ...          ...\n",
       "18244  2018      2    4            6\n",
       "18245  2018      1   28            6\n",
       "18246  2018      1   21            6\n",
       "18247  2018      1   14            6\n",
       "18248  2018      1    7            6\n",
       "\n",
       "[18249 rows x 4 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#covert date into 4 features\n",
    "from datetime import datetime\n",
    "\n",
    "df['Date'] = df['Date'].apply(lambda x: datetime.strptime(x, '%Y-%m-%d'))\n",
    "\n",
    "df['month'] = df['Date'].dt.month\n",
    "df['day'] = df['Date'].dt.day\n",
    "# monday = 0\n",
    "df['day of week'] = df['Date'].dt.dayofweek\n",
    "dates = ['year', 'month', 'day', 'day of week']\n",
    "df[dates]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "3bebbb7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x648 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute the correlation matrix\n",
    "corr = df.corr()\n",
    "\n",
    "# Generate a mask for the upper triangle\n",
    "mask = np.triu(np.ones_like(corr, dtype=bool))\n",
    "\n",
    "# Set up the matplotlib figure\n",
    "f, ax = plt.subplots(figsize=(11, 9))\n",
    "\n",
    "# Generate a custom diverging colormap\n",
    "cmap = sns.diverging_palette(230, 20, as_cmap=True)\n",
    "\n",
    "# Draw the heatmap with the mask and correct aspect ratio\n",
    "sns.heatmap(corr, mask=mask, cmap=cmap, vmax=1, center=0,\n",
    "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "7197a400",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr());"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "daac3182",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2,2, figsize=(20,10))\n",
    "\n",
    "sns.countplot(x='year', data=df, ax=ax[0,0])\n",
    "sns.countplot(x='month', data=df, ax=ax[0,1])\n",
    "sns.countplot(x='day', data=df, ax=ax[1,0])\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "cfd6f2ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, 1, figsize=(10,6))\n",
    "\n",
    "df['year_month'] = df['Date'].dt.to_period('M')\n",
    "grouped = df.groupby('year_month')[['AveragePrice', 'Total Volume']].mean()\n",
    "\n",
    "ax[0].plot(grouped.index.astype(str), grouped['AveragePrice'])\n",
    "ax[0].tick_params(labelrotation=90)\n",
    "ax[0].set_ylabel('AveragePrice')\n",
    "\n",
    "\n",
    "ax[1].plot(grouped.index.astype(str), grouped['Total Volume'])\n",
    "ax[1].tick_params(labelrotation=90)\n",
    "ax[1].set_ylabel('Total Volume')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "fb608971",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZotTUVO3du1e33357wEEHDRqkgwcPNtjudrvVrl07/+O2bdvK7XYHHM/j8ai0tDTg9wWSlpbW7DEQnYIx/4KBOYzmiJR5HGydOnXShAkTzI4BAIhgAUvPiBEjNHDgQB04cEBdu3ZVhw4dfvaLJSQkqKqqyv+4qqqqXglqjMPh4Jc9mIr5Byto7jyO1NJ01VVX6dFHH9U555zj33bjjTeaFwgAEHEaLT2LFy/WuHHjlJubK5vNVm/f/Pnzf9aLpaSkaN++faqsrFSbNm20detWjR079meNBQCAJP39739X9+7dVVZWJkkN1iwAABotPQMGDJAkZWVl6ZRTTmnWi6xbt07V1dXKzs7WlClTNHbsWBmGoWHDhunMM89s1tgAgOhmt9v1wAMPmB0DABDBGi09PXv2lCT95S9/0cqVK3/ywF26dPFfknrIkCH+7QMGDPAXKgAAmqtz58568skndd555/mP8lx22WUmpwIARJKAn+lJTEzUs88+q+TkZMXEfH+xNxYTAECk8Hq9+vzzz/X555/7t7FOAQB+LGDpad++vXbu3KmdO3f6t7GYAAAixdy5c82OAACIcE2Wnl27dmncuHHq2rVruPIAAPCT/Pgf4iorK9W1a1e99tprJiYCAESaRkvPn/70J33wwQeqqanRmDFjdMMNN4QzFwAAJ+Xdd9/1f/3FF1/o8ccfNzENACASxTS244MPPtCqVav0/PPP65VXXglnJgAAfpazzjpLe/fuNTsGACDCNHqkx263S5Li4+Pl9XrDFggAgJ/ix/eTKy8v12mnnWZyIgBApAl4IQMAACJZTk6O/2uHw6ELLrjAxDQAgEjUaOn55z//qZycHBmGoT179vi/ttlsWrVqVTgzAgDQQFFR0Qm379y5U9nZ2WFOAwCIZI2WnrVr14YzBwAAP0lFRYXZEQAALUSjpeess86SJLndbhUWFqq8vFxXXXWVevToEbZwAAA0Zvz48f6v//u//1u7d+9WcnKyrr76ahNTAQAiUaNXb/vB1KlT1bVrV+3bt09JSUm6//77w5ELAICTMn/+fK1Zs0atWrXSyy+/rIcffjjgc3w+n/Ly8pSdnS2Xy6V9+/bV2//MM89o6NChGjZsmN58881QRQcAhEnACxlUVlZq+PDhWrt2rTIyMuTz+cKRCwCAk/Lhhx/6P2s6ZswYjRw5MuBzNmzYoJqaGhUVFamkpET5+flasmSJJOno0aNavny53njjDR07dkw33nijMjMzQ/oeAAChFfBIjySVlZVJkr766ivFxsaGNBAAAD+F1+v1/4PcDxfcCaS4uFj9+/eXJKWnp2vHjh3+ffHx8ercubOOHTumY8eOndR4AIDIFvBIz/3336+pU6eqrKxMEydO1IwZM8KRCwCAk5KVlaVRo0bpF7/4hbZv366srKyAz3G73UpISPA/jo2NldfrVVzc98tip06ddO2116qurk533nlnyLIDAMIjYOnp0aNHo5cFBQDALH/+8581cuRI3Xbbbbrsssu0d+9eDR8+XKmpqQGfm5CQoKqqKv9jn8/nLzybNm1SeXm53nrrLUnS2LFjlZGRod69ezc6nsfjUWlpaTPfkZSWltbsMRCdgjH/ACsLWHr69++vw4cPq3379qqsrJTdbldSUpJmzJihfv36hSMjAAANJCYm6q677tLpp5+u7Oxs/frXv1ZMzEmdta2MjAxt3LhRWVlZKikpqVeUEhMT1bp1a9ntdtlsNrVr105Hjx5tcjyHw0FhgamYf0DT5T9g6fnlL3+p8ePHq3v37tq/f78ef/xx3X333frjH/9I6QEAmObWW2/Vrbfeqk8++URr1qzRwoULdfXVV2vkyJHq3Llzk8/NzMzU5s2b/TfenjNnjpYuXSqn06mBAwfqvffe08iRIxUTE6OMjAzWOwBo4QKWnq+++krdu3eXJDmdTv373/9Wt27duKABACAi9OrVS7169VJNTY0WLVqka665Rtu3b2/yOTExMZo1a1a9bSkpKf6vJ06cqIkTJ4YkLwAg/AKWntNPP12PPvqo+vTpo48//lhJSUnavHmzWrVqFY58AAA06d///rfWrl2r1157TSkpKXryySfNjgQAiDABS88jjzyioqIibdq0SampqZowYYL+9a9/acGCBeHIBwDACa1Zs0YvvfSSKisrNWzYMC1dulTt27c3OxYAIAIFLD2xsbHq1auX0tLSZBiG3nzzTV133XXhyAYAQKO2bNmie+65R3379jU7CgAgwgUsPePHj1dtba3Ky8tVV1enM844g9IDADBdfn6+2REAAC1EwGt7HjlyRH/5y1/Uu3dvrVmzRh6PJxy5AAAAACAoApae1q1bS5KOHTum1q1by2azBRzU5/MpLy9P2dnZcrlc2rdvX739zzzzjIYOHaphw4bpzTff/JnRAQAAACCwgKe3/frXv9aiRYvUs2dPjRw5Um3atAk46IYNG1RTU6OioiKVlJQoPz9fS5YskSQdPXpUy5cv1xtvvKFjx47pxhtvVGZmZvPfCQAgquTm5jb6D3Hz588PcxoAQCQLWHpSUlJ08cUXy2az6YorrlC3bt0CDlpcXKz+/ftLktLT07Vjxw7/vvj4eHXu3FnHjh3TsWPHTurIEQAA/7+cnByzIwAAWoiApaegoECXXHKJJKlHjx4nNajb7VZCQoL/cWxsrLxer+Livn+5Tp066dprr1VdXZ3uvPPOgON5PB6Vlpae1Gs3JS0trdljIDoFY/4FA3MYzREp8zhYLrroIklSZWWl3n33XXm9XhmGofLycv8+AACkkyg9NptNd999t5KTkxUT8/1HgHJzc5t8TkJCgqqqqvyPfT6fv/Bs2rRJ5eXleuuttyRJY8eOVUZGhnr37t3oeA6Hg1/2YCrmH6ygufM4UkvT+PHj1b17d+3atUsOh0Px8fFmRwIARJiAFzIYNmyYrr76aqWkpCg5OVnJyckBB83IyNCmTZskSSUlJUpNTfXvS0xMVOvWrWW32+VwONSuXTsdPXq0GW8BABDNDMPQrFmzlJycrKVLl6qystLsSACACBPwSM+QIUP00ksv6csvv9Qll1yic889N+CgmZmZ2rx5s3JycmQYhubMmaOlS5fK6XRq4MCBeu+99zRy5EjFxMQoIyND/fr1C8qbAQBEn9jYWHk8Hv/nROvq6syOBACIMAFLz4wZM3TGGWfovffeU69evTR58mQVFhY2+ZyYmBjNmjWr3raUlBT/1xMnTtTEiRN/ZmQAAP7PzTffrGeffVb9+vXTFVdcob59+5odCQAQYQKWnv3792v27NnaunWrBgwYoKeeeiocuQAAOCmdO3fWoEGDJEmDBw/Wv/71L5MTAQAiTcDSU1dXp8OHD8tms8ntdvsvZgAAgJm2bt2qPXv2aNmyZfrtb38r6fsL5zz//PNav369yekAAJEkYOn5wx/+oFGjRqmiokLZ2dm6//77w5ELAIAmnXLKKfr6669VU1OjiooKSd9fcfSPf/yjyckAAJEmYOlp166d/uu//kuHDx9W+/btuZkoACAipKamKjU1VSNGjJDD4dD+/fvVpUsXdejQwexoAIAIE/BctYULFyonJ0cbNmzQsWPHwpEJAICTVlxcrOzsbD3xxBPKzs7WK6+8YnYkAECECXik54knnlBFRYVeeeUV3XbbbUpJSdHs2bPDkQ0AgICeffZZrVmzRm3btpXb7daYMWN0ww03mB0LABBBTuqqBF6vVzU1NfL5fIqNjQ11JgAATprNZlPbtm0lSQkJCXI4HCYnAgBEmoBHekaPHq2amhoNHz5cjz32mNauXRuOXAAAnJSuXbsqPz9fF154obZu3Sqn02l2JABAhAl4pOf+++/X1KlTtWXLFg0fPlxfffVVOHIBANCke+65R5I0d+5cde3aVe+99566du2qBx980NxgAICI0+iRnpqaGr366qt6/vnnZbfb5Xa7tWHDBrVu3Tqc+QAAOKHDhw9LkuLi4nTzzTebnAYAEMkaLT0DBgzQddddp0cffVRnn322br/9dgoPACBiHDhwQAsWLDjhvtzc3DCnAQBEskZLz5gxY7Ru3Tp98cUXGj58uAzDCGcuAACa1Lp1ayUnJ5sdAwDQAjRaen73u9/pd7/7nbZs2aIXXnhBO3bs0Lx583TDDTcoNTU1nBkBAGggKSlJv/nNb8yOAQBoAQJeyOCiiy7SvHnz9Oabb6pjx4669957w5ELAIAmXXDBBWZHAAC0EAEvWf2DU045RS6XSy6XK5R5AAA4KZMnT/7Zz/X5fJo5c6Y+/fRT2e12PfTQQ+rWrZt//zvvvKNFixbJMAydf/75mjFjhmw2WzBiAwBMcFI3JwUAwEo2bNigmpoaFRUVadKkScrPz/fvc7vdmjdvnp544gm98MILOuuss3TkyBET0wIAmovSAwCIOsXFxerfv78kKT09XTt27PDv+/jjj5WamqqHH35YN910k5KSktShQwezogIAguCkT28DAMAq3G63EhIS/I9jY2Pl9XoVFxenI0eO6IMPPtDLL7+sNm3a6Oabb1Z6enqTV4rzeDwqLS1tdq60tLRmj4HoFIz5B1gZpQcAEHUSEhJUVVXlf+zz+RQX9/2SeOqpp6pXr146/fTTJUkXXnihSktLmyw9DoeDwgJTMf+Apss/p7cBAKJORkaGNm3aJEkqKSmpdyuG888/X7t27dLhw4fl9Xq1bds2nXPOOWZFBQAEAUd6AABRJzMzU5s3b1ZOTo4Mw9CcOXO0dOlSOZ1ODRw4UJMmTdLtt98uSbrmmmu4Px0AtHCUHgBA1ImJidGsWbPqbUtJSfF/fe211+raa68NdywAQIhwehsAAAAASwvJkR5u+gYAAAAgUoTkSA83fQMAAAAQKUJSerjpGwAAAIBIEZLT27jpG6wmUm76xhxGc0TKPAYAINxCUnq46RushvkHK2juPKY0AQBaqpCc3sZN3wAAAABEipAc6eGmbwAAAAAiRUhKDzd9AwAAABApuDkpAAAAAEuj9AAAAACwNEoPAAAAAEuj9AAAAACwNEoPAAAAAEuj9AAAAACwNEoPAAAAAEuj9AAAAACwNEoPAAAAAEuj9AAAAACwNEoPAAAAAEuj9AAAAACwNEoPAAAAAEuj9AAAAACwNEoPAAAAAEuj9AAAAACwNEoPAAAAAEuj9AAAoo7P51NeXp6ys7Plcrm0b9++E37P7bffrpUrV5qQEAAQTJQeAEDU2bBhg2pqalRUVKRJkyYpPz+/wfcsXLhQR48eNSEdACDYKD0AgKhTXFys/v37S5LS09O1Y8eOevtff/112Ww2//cAAFq2OLMDAAAQbm63WwkJCf7HsbGx8nq9iouL065du7R+/Xo99thjWrRo0UmN5/F4VFpa2uxcaWlpzR4D0SkY8w+wMkoPACDqJCQkqKqqyv/Y5/MpLu77JfHll1/WoUOHNGbMGH3xxRdq1aqVzjrrLF1++eWNjudwOCgsMBXzD2i6/FN6AABRJyMjQxs3blRWVpZKSkqUmprq33fvvff6vy4oKFBSUlKThQcAEPlC8pkerooDAIhkmZmZstvtysnJ0dy5c3Xfffdp6dKleuutt8yOBgAIgZAc6fnxVXFKSkqUn5+vJUuW1PserooDADBLTEyMZs2aVW9bSkpKg++bMGFCuCIBAEIoJEd6uCoOAAAAgEgRkiM9XBUHVhMpV8VhDqM5ImUeAwAQbiEpPVwVB1bD/IMVNHceU5oAAC1VSEoPV8UBAAAAEClCUnoyMzO1efNm5eTkyDAMzZkzR0uXLpXT6dTAgQND8ZIAAAAAcEIhKT1cFQcAAABApAjJ1dsAAAAAIFJQegAAAABYGqUHAAAAgKVRegAAAABYGqUHAAAAgKVRegAAAABYGqUHAAAAgKVRegAAAABYGqUHAAAAgKVRegAAAABYGqUHAAAAgKVRegAAAABYGqUHAAAAgKVRegAAAABYGqUHAAAAgKVRegAAAABYGqUHAAAAgKVRegAAAABYGqUHAAAAgKVRegAAAABYWpzZAQAACDefz6eZM2fq008/ld1u10MPPaRu3br59y9btkyvvvqqJOmKK67Q+PHjzYoKAAgCjvQAAKLOhg0bVFNTo6KiIk2aNEn5+fn+fQcOHNDatWu1atUqrV69Wu+++6527txpYloAQHNxpAcAEHWKi4vVv39/SVJ6erp27Njh39exY0c9/fTTio2NlSR5vV45HA5TcgIAgiMkpYfTBgAAkcztdishIcH/ODY2Vl6vV3FxcWrVqpU6dOggwzD0yCOP6LzzzlNycnKT43k8HpWWljY7V1paWrPHQHQKxvwDrCwkpefHpw2UlJQoPz9fS5YskfR/pw288MILiomJ0ahRo3T11VerZ8+eoYgCAEADCQkJqqqq8j/2+XyKi/u/JdHj8Wjq1Klq27atZsyYEXA8h8NBYYGpmH9A0+U/JJ/pOdnTBmw2G6cNAADCLiMjQ5s2bZIklZSUKDU11b/PMAyNGzdOPXr00KxZs/ynuQEAWq6QHOkJ9mkDAAAEU2ZmpjZv3qycnBwZhqE5c+Zo6dKlcjqd8vl82rJli2pqavQ///M/kqTc3Fz16dPH5NQAgJ8rJKUn2KcNcK40zBYp50ozh9EckTKPI0FMTIxmzZpVb1tKSor/608++STckQAAIRSS0pORkaGNGzcqKyur0dMGLr74Yt1xxx0nNR7nSsNszD9YQXPnMaUJANBShaT0cNoAAAAAgEgRktLDaQMAAAAAIkVIrt4GAAAAAJGC0gMAAADA0ig9AAAAACyN0gMAAADA0ig9AAAAACyN0gMAAADA0ig9AAAAACyN0gMAAADA0ig9AAAAACyN0gMAAADA0ig9AAAAACyN0gMAAADA0ig9AAAAACyN0gMAAADA0ig9AAAAACyN0gMAAADA0ig9AAAAACyN0gMAAADA0ig9AAAAACyN0gMAAICgMby1ZkdACxTqeRMX0tEBAAAQVWxxrVS+5F6zY6CFOeOuR0I6Pkd6AAAAAFgapQcAAACApYWk9Ph8PuXl5Sk7O1sul0v79u2rt3/16tUaOnSoRo4cqY0bN4YiAgAAjWKdAoDoEpLP9GzYsEE1NTUqKipSSUmJ8vPztWTJEklSRUWFVqxYob/97W/yeDy66aab1K9fP9nt9lBEAQCgAdYpAIguITnSU1xcrP79+0uS0tPTtWPHDv++7du3q0+fPrLb7WrXrp2cTqd27twZihgAAJwQ6xQARJeQlB63262EhAT/49jYWHm9Xv++du3a+fe1bdtWbrc7FDEAADgh1ikAiC4hOb0tISFBVVVV/sc+n09xcXEn3FdVVVVvcTkRj8ej0tLSoGS7/exfBmUcRI9gzb2gufK3ZidAC/RNEOaxx+MJQpLIwDoFK4m4dUpircJPFup1KiSlJyMjQxs3blRWVpZKSkqUmprq39e7d28tXLhQHo9HNTU1Kisrq7f/RNLT00MREwAQpVinACC62AzDMII9qM/n08yZM7Vr1y4ZhqE5c+Zo06ZNcjqdGjhwoFavXq2ioiIZhqE777xTgwYNCnYEAAAaxToFANElJKUHAAAAACIFNycFAAAAYGmUHgAAAACWRukBAAAAYGmUHpzQm2++qUOHDqmiokIzZ84M2rj9+vUL2ljAT7VmzRq99dZbZscAEASsU7Ai1qnQ4UIGOCGXy6WZM2cqJSUlqOP269dPmzdvDuqYAIDowzoF4KcIyX160HzHjx/Xfffdpy+//FK1tbWaOnWqVq1apYMHD6qurk6//e1vlZWVJZfLpZ49e2r37t1yu93685//rLfeektHjx7V+PHjVVNTo+uvv15r165VUVGR1q9fL5vNpqysLI0ePVpTpkyR3W7XF198ofLycuXn56uiokKlpaWaPHmy5s2bp8mTJ2v16tXavHmzFi5cKIfDoVNPPVVz5sxRaWmpCgsL1apVKx08eFBZWVm66667tGvXLuXn56uurk5HjhzRzJkzlZGRYfYfKyJYbW2t7rvvvnpzfOXKlerQoYO+/fZbLV68WFOmTFF5ebk6deqkDz/8UO+++662bNmixx9/XIZhqKqqSvPnz1erVq00adIkdezYUQcOHFCvXr30wAMPqKCgQElJScrJydGDDz6o7du3q7a2VhMmTNDVV19t9h8B0KKwTiHasE61cAYi0tKlS4158+YZhmEYn332mbFo0SJj9uzZhmEYxnfffWdkZmYa33zzjXHLLbcYa9euNQzDMBYsWGA8+eSTRmVlpZGVlWX4fD7j73//u5Gfn2/s3r3byMnJMbxer+H1eg2Xy2WUlZUZkydPNpYsWWIYhmEUFRUZ06dPNwzDMG655RZjz549xoEDB4wRI0YYPp/PuOqqq4yvvvrKMAzDWLZsmZGfn2+8//77xuDBg43a2lqjqqrKyMjIMAzDMF599VVj586dhmEYxtq1a43777/fMAzD+NWvfhWmP0G0NCtWrGgwx6+99lrjjTfeMAzj+zn38MMPG4ZhGHv27DF69uxpGIZhPPfcc/55uWTJEmPx4sXGgQMHjIsuusj47rvvDK/Xa1x55ZVGeXm58dhjjxl//etfjTfeeMO45557DMMwjMrKSuNPf/pTmN8t0PKxTiHasE61bBzpiVB79+7V5ZdfLkk6++yzVVFRoV/96leSpISEBKWkpOjAgQOSpPPOO0+S1LFjR3399ddKTExUWlqaiouL9dJLL2ny5Mn69NNP9eWXX+rWW2+VJH377bfat2+fJCktLc3//I8++uiEeY4cOaKEhASdeeaZkqRf/vKXWrBgga688kqlpqYqLi5OcXFxat26tSTpjDPO0OLFi9W6dWtVVVUpISEhBH9KsJKysrIGc3zz5s1KTk727//h70RKSoo6dOggSTrzzDM1e/ZstWnTRocOHfL/S63T6fTPu9NPP10ej8f/Wp999pnS09MlSYmJibrnnnvC8RYBS2GdQrRhnWrZuJBBhEpJSdEnn3wiSTpw4IBeffVVbd26VZLkdru1a9cudenSpdHnjxw5Us8++6yOHz+ulJQUde/eXeecc46WL1+uFStWaOjQoerRo4ckyWazNXi+zWaT8aOPe7Vv315ut1vl5eWSpC1btujss89u9PmzZ8/WxIkT9fDDDys1NbXeWMCJpKSknHCO/zC/UlNT9fHHH0uS9u/fryNHjkiSpk+frjlz5ig/P19nnHGGf66daF7+oHv37v6/X999953Gjh0bsvcFWBXrFKIN61TLxpGeCJWTk6OpU6fqlltuUV1dnZ5++mk9//zzGjVqlDwej8aPH6/TTjut0edfdNFFmj59uu666y5JUs+ePXXppZdq1KhRqqmpUe/evf3/GnYiffr00b333qsHH3xQ0vd/MR966CFNmDBBNptNiYmJmjt3rnbv3n3C519//fX6j//4D51yyinq2LGj/y8+0JiRI0dq+vTp9eb4mjVr/PuHDx+uKVOm6Oabb1bnzp3lcDgkfT/Xbr75ZsXHxyspKcn/C09TBg4cqH/84x8aNWqU6urqdPfdd4fsfQFWxTqFaMM61bJx9TYALcJHH32k6upqXXbZZfr88891++23a8OGDWbHAgBAEutUpKP0AGgRKioqlJubq9raWnm9Xk2cONF/7jQAAGZjnYpslB4AAAAAlsaFDAAAAABYGqUHAAAAgKVRegAAAABYGqUHCAKPx6MXXnjB7BgAAJwQ6xSiHaUHCIKKigoWEwBAxGKdQrTj5qRAEDzxxBPas2ePevToofXr1+vcc8/VO++8o40bN+q0007T3r179c033+jo0aOaNm2aLrzwQr322mtatmyZYmJi1LdvX/3nf/6n2W8DAGBRrFOIdhzpAYLg97//vc455xzNmTNHL730kiTpb3/7m0aMGCFJat26tZYvX6558+Zp1qxZqqysVEFBgZYtW6aVK1fq0KFD2rx5s5lvAQBgYaxTiHaUHiCIBg8erLffflvffPONDh06pPPPP1+SdMkll0iSzj33XH399dfav3+/Dh8+rDvuuEMul0tlZWXav3+/mdEBAFGAdQrRitPbgCCIiYmRz+dTmzZtdPHFF2v27Nm6/vrr/fv/+c9/6oYbbtCuXbt05plnqkuXLurUqZOeeeYZtWrVSmvWrFFaWpqJ7wAAYGWsU4h2lB4gCE477TTV1tZq3rx5GjlypG666SbNnDnTv7+0tFRjxozRsWPH9OCDD6pDhw669dZb5XK5VFdXp7POOkuDBw827w0AACyNdQrRzmYYhmF2CMBKtm/frueee06PPPKIJKmgoEBJSUkaNWqUyckAAGCdQnTiSA8QRM8995xefPFFLVy40OwoAAA0wDqFaMWRHgAAAACWxtXbAAAAAFgapQcAAACApVF6AAAAAFgapQcAAACApVF6AAAAAFgapQcAAACApf0vlfTIWIgzphUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1008x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "type\n",
       "conventional    1.158040\n",
       "organic         1.653999\n",
       "Name: AveragePrice, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "type\n",
       "conventional    1.508722e+10\n",
       "organic         4.361817e+08\n",
       "Name: Total Volume, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(14,5))\n",
    "\n",
    "sns.barplot(x='type', y='AveragePrice', data=df, palette='Set2', ax=ax[0])\n",
    "sns.barplot(x='type', y='Total Volume', data=df, palette='Set2', ax=ax[1], estimator=sum, ci=None)\n",
    "plt.show()\n",
    "\n",
    "display(df.groupby('type')['AveragePrice'].mean())\n",
    "display(df.groupby('type')['Total Volume'].sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e300e7df",
   "metadata": {},
   "source": [
    "## Conclusion on EDA: \n",
    "balance data, sales are seasonal trend, Jan has the heightest sales, organic has heiger average price then traditional, but traditinal sales more. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a21b76d5",
   "metadata": {},
   "source": [
    "## Regression on price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "ab60d5af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>AveragePrice</th>\n",
       "      <th>Total Volume</th>\n",
       "      <th>4046</th>\n",
       "      <th>4225</th>\n",
       "      <th>4770</th>\n",
       "      <th>Total Bags</th>\n",
       "      <th>Small Bags</th>\n",
       "      <th>Large Bags</th>\n",
       "      <th>XLarge Bags</th>\n",
       "      <th>type</th>\n",
       "      <th>year</th>\n",
       "      <th>region</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>day of week</th>\n",
       "      <th>year_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-12-27</td>\n",
       "      <td>1.33</td>\n",
       "      <td>64236.62</td>\n",
       "      <td>1036.74</td>\n",
       "      <td>54454.85</td>\n",
       "      <td>48.16</td>\n",
       "      <td>8696.87</td>\n",
       "      <td>8603.62</td>\n",
       "      <td>93.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>conventional</td>\n",
       "      <td>2015</td>\n",
       "      <td>Albany</td>\n",
       "      <td>12</td>\n",
       "      <td>27</td>\n",
       "      <td>6</td>\n",
       "      <td>2015-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-12-20</td>\n",
       "      <td>1.35</td>\n",
       "      <td>54876.98</td>\n",
       "      <td>674.28</td>\n",
       "      <td>44638.81</td>\n",
       "      <td>58.33</td>\n",
       "      <td>9505.56</td>\n",
       "      <td>9408.07</td>\n",
       "      <td>97.49</td>\n",
       "      <td>0.0</td>\n",
       "      <td>conventional</td>\n",
       "      <td>2015</td>\n",
       "      <td>Albany</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>2015-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-12-13</td>\n",
       "      <td>0.93</td>\n",
       "      <td>118220.22</td>\n",
       "      <td>794.70</td>\n",
       "      <td>109149.67</td>\n",
       "      <td>130.50</td>\n",
       "      <td>8145.35</td>\n",
       "      <td>8042.21</td>\n",
       "      <td>103.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>conventional</td>\n",
       "      <td>2015</td>\n",
       "      <td>Albany</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>2015-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-12-06</td>\n",
       "      <td>1.08</td>\n",
       "      <td>78992.15</td>\n",
       "      <td>1132.00</td>\n",
       "      <td>71976.41</td>\n",
       "      <td>72.58</td>\n",
       "      <td>5811.16</td>\n",
       "      <td>5677.40</td>\n",
       "      <td>133.76</td>\n",
       "      <td>0.0</td>\n",
       "      <td>conventional</td>\n",
       "      <td>2015</td>\n",
       "      <td>Albany</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>2015-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-11-29</td>\n",
       "      <td>1.28</td>\n",
       "      <td>51039.60</td>\n",
       "      <td>941.48</td>\n",
       "      <td>43838.39</td>\n",
       "      <td>75.78</td>\n",
       "      <td>6183.95</td>\n",
       "      <td>5986.26</td>\n",
       "      <td>197.69</td>\n",
       "      <td>0.0</td>\n",
       "      <td>conventional</td>\n",
       "      <td>2015</td>\n",
       "      <td>Albany</td>\n",
       "      <td>11</td>\n",
       "      <td>29</td>\n",
       "      <td>6</td>\n",
       "      <td>2015-11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date  AveragePrice  Total Volume     4046       4225    4770  \\\n",
       "0 2015-12-27          1.33      64236.62  1036.74   54454.85   48.16   \n",
       "1 2015-12-20          1.35      54876.98   674.28   44638.81   58.33   \n",
       "2 2015-12-13          0.93     118220.22   794.70  109149.67  130.50   \n",
       "3 2015-12-06          1.08      78992.15  1132.00   71976.41   72.58   \n",
       "4 2015-11-29          1.28      51039.60   941.48   43838.39   75.78   \n",
       "\n",
       "   Total Bags  Small Bags  Large Bags  XLarge Bags          type  year  \\\n",
       "0     8696.87     8603.62       93.25          0.0  conventional  2015   \n",
       "1     9505.56     9408.07       97.49          0.0  conventional  2015   \n",
       "2     8145.35     8042.21      103.14          0.0  conventional  2015   \n",
       "3     5811.16     5677.40      133.76          0.0  conventional  2015   \n",
       "4     6183.95     5986.26      197.69          0.0  conventional  2015   \n",
       "\n",
       "   region  month  day  day of week year_month  \n",
       "0  Albany     12   27            6    2015-12  \n",
       "1  Albany     12   20            6    2015-12  \n",
       "2  Albany     12   13            6    2015-12  \n",
       "3  Albany     12    6            6    2015-12  \n",
       "4  Albany     11   29            6    2015-11  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "5a93a6cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(18249, 9)\n"
     ]
    }
   ],
   "source": [
    "df_reg = df.drop(['Date', '4046','4225','4770','year_month', 'type', 'region'],axis=1)\n",
    "target = df_reg['AveragePrice']\n",
    "features = df_reg.drop(['AveragePrice'],axis=1)\n",
    "print(features.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "2b7e6d07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training score : 0.97\n",
      "testing score: 0.77\n",
      "mean abs error: 0.14\n",
      "\n",
      " feature importances:\n",
      "                     0\n",
      "0.468784  Total Volume\n",
      "0.118533    Large Bags\n",
      "0.102070         month\n",
      "0.089935    Total Bags\n",
      "0.086894    Small Bags\n",
      "0.074564          year\n",
      "0.030498   XLarge Bags\n",
      "0.028722           day\n",
      "0.000000   day of week\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "X_train, X_test, y_train, y_test = train_test_split(features.values, target.values, random_state=42)\n",
    "\n",
    "model = RandomForestRegressor().fit(X_train, y_train)\n",
    "pred = model.predict(X_test)\n",
    "print(\"training score : {:.2f}\".format(model.score(X_train, y_train)))\n",
    "print(\"testing score: {:.2f}\".format(model.score(X_test, y_test)))\n",
    "print('mean abs error: {:.2f}'.format(mean_absolute_error(pred, y_test)))\n",
    "\n",
    "print(\"\\n\", \"feature importances:\")\n",
    "feature_importance = pd.DataFrame(list(features.keys()), model.feature_importances_)\n",
    "print(feature_importance.sort_index(ascending=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "d8162881",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training score : 0.06\n",
      "testing score: 0.06\n",
      "mean abs error: 0.31\n"
     ]
    }
   ],
   "source": [
    "# lasso\n",
    "from sklearn import linear_model\n",
    "model = linear_model.Lasso(alpha=0.1).fit(X_train, y_train)\n",
    "\n",
    "pred = model.predict(X_test)\n",
    "print(\"training score : {:.2f}\".format(model.score(X_train, y_train)))\n",
    "print(\"testing score: {:.2f}\".format(model.score(X_test, y_test)))\n",
    "print('mean abs error: {:.2f}'.format(mean_absolute_error(pred, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "f06ab9dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training score : 0.06\n",
      "testing score: 0.06\n",
      "mean abs error: 0.23\n"
     ]
    }
   ],
   "source": [
    "# on log transform\n",
    "from sklearn import linear_model\n",
    "model = linear_model.Lasso(alpha=0.1).fit(X_train, np.log(y_train))\n",
    "\n",
    "pred = model.predict(X_test)\n",
    "print(\"training score : {:.2f}\".format(model.score(X_train, np.log(y_train))))\n",
    "print(\"testing score: {:.2f}\".format(model.score(X_test, np.log(y_test))))\n",
    "print('mean abs error: {:.2f}'.format(mean_absolute_error(pred, np.log(y_test))))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfd81541",
   "metadata": {},
   "source": [
    "### Conclusion: \n",
    "\n",
    "1. Use mean absolute error to reduce the effect on outliers.\n",
    "2. Random forest has best mean abs error, 0.14, lasso's error is higer 0.31, but after log transform error reduce to 0.23."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7f4b527",
   "metadata": {},
   "source": [
    "## Classfication"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "98308ebe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(18249, 10)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AveragePrice</th>\n",
       "      <th>Total Volume</th>\n",
       "      <th>Total Bags</th>\n",
       "      <th>Small Bags</th>\n",
       "      <th>Large Bags</th>\n",
       "      <th>XLarge Bags</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>day of week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.33</td>\n",
       "      <td>64236.62</td>\n",
       "      <td>8696.87</td>\n",
       "      <td>8603.62</td>\n",
       "      <td>93.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>27</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.35</td>\n",
       "      <td>54876.98</td>\n",
       "      <td>9505.56</td>\n",
       "      <td>9408.07</td>\n",
       "      <td>97.49</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.93</td>\n",
       "      <td>118220.22</td>\n",
       "      <td>8145.35</td>\n",
       "      <td>8042.21</td>\n",
       "      <td>103.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.08</td>\n",
       "      <td>78992.15</td>\n",
       "      <td>5811.16</td>\n",
       "      <td>5677.40</td>\n",
       "      <td>133.76</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.28</td>\n",
       "      <td>51039.60</td>\n",
       "      <td>6183.95</td>\n",
       "      <td>5986.26</td>\n",
       "      <td>197.69</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>11</td>\n",
       "      <td>29</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AveragePrice  Total Volume  Total Bags  Small Bags  Large Bags  \\\n",
       "0          1.33      64236.62     8696.87     8603.62       93.25   \n",
       "1          1.35      54876.98     9505.56     9408.07       97.49   \n",
       "2          0.93     118220.22     8145.35     8042.21      103.14   \n",
       "3          1.08      78992.15     5811.16     5677.40      133.76   \n",
       "4          1.28      51039.60     6183.95     5986.26      197.69   \n",
       "\n",
       "   XLarge Bags  year  month  day  day of week  \n",
       "0          0.0  2015     12   27            6  \n",
       "1          0.0  2015     12   20            6  \n",
       "2          0.0  2015     12   13            6  \n",
       "3          0.0  2015     12    6            6  \n",
       "4          0.0  2015     11   29            6  "
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "le = preprocessing.LabelEncoder()\n",
    "\n",
    "features = df.drop(['Date', '4046','4225','4770','type','year_month', 'region'],axis=1)\n",
    "features['target'] = le.fit_transform(df['type'])\n",
    "target = features['target']\n",
    "features = features.drop(['target'],axis=1)\n",
    "\n",
    "\n",
    "print(features.shape)\n",
    "features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "100ecb5b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training score : 1.00\n",
      "testing score: 0.99\n",
      "f1: 0.99\n",
      "\n",
      " feature importances:\n",
      "                     0\n",
      "0.350844  Total Volume\n",
      "0.187350    Small Bags\n",
      "0.185342    Total Bags\n",
      "0.105907  AveragePrice\n",
      "0.093571   XLarge Bags\n",
      "0.037709    Large Bags\n",
      "0.017594          year\n",
      "0.014507         month\n",
      "0.007176           day\n",
      "0.000000   day of week\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(features.values, target.values, \n",
    "                                                    test_size=0.3,random_state=42)\n",
    "\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import f1_score\n",
    "\n",
    "model = RandomForestClassifier().fit(X_train, y_train)\n",
    "pred = model.predict(X_test)\n",
    "print(\"training score : {:.2f}\".format(model.score(X_train, y_train)))\n",
    "print(\"testing score: {:.2f}\".format(model.score(X_test, y_test)))\n",
    "print('f1: {:.2f}'.format(f1_score(pred, y_test)))\n",
    "\n",
    "print(\"\\n\", \"feature importances:\")\n",
    "feature_importance = pd.DataFrame(list(features.keys()), model.feature_importances_)\n",
    "print(feature_importance.sort_index(ascending=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "5f599b0c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training score : 0.92\n",
      "testing score: 0.93\n",
      "f1: 0.93\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "model = LogisticRegression().fit(X_train, y_train)\n",
    "pred = model.predict(X_test)\n",
    "print(\"training score : {:.2f}\".format(model.score(X_train, y_train)))\n",
    "print(\"testing score: {:.2f}\".format(model.score(X_test, y_test)))\n",
    "print('f1: {:.2f}'.format(f1_score(pred, y_test)))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e32f8b4",
   "metadata": {},
   "source": [
    "### # add regularization and CV search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "89c93b9a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training score : 0.94\n",
      "testing score: 0.95\n",
      "f1: 0.95\n"
     ]
    }
   ],
   "source": [
    "\n",
    "model = LogisticRegression(penalty='l1',solver='liblinear')\n",
    "model.fit(X_train, y_train)\n",
    "pred = model.predict(X_test)\n",
    "print(\"training score : {:.2f}\".format(model.score(X_train, y_train)))\n",
    "print(\"testing score: {:.2f}\".format(model.score(X_test, y_test)))\n",
    "print('f1: {:.2f}'.format(f1_score(pred, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "53adc6cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=0.1, penalty='l1', solver='liblinear')"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "parameters = {'penalty':('l1', 'l2'), 'C':[0.1, 0.2 ,0.3 ,0.5, 0.8, 1, 1.3]}\n",
    "lr = LogisticRegression(solver='liblinear')\n",
    "clf = GridSearchCV(lr, parameters)\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "clf.best_params_\n",
    "clf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "2634c534",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training score : 0.94\n",
      "testing score: 0.95\n"
     ]
    }
   ],
   "source": [
    "model = LogisticRegression(C=0.1, penalty='l1', solver='liblinear')\n",
    "model.fit(X_train, y_train)\n",
    "pred = model.predict(X_test)\n",
    "print(\"training score : {:.2f}\".format(model.score(X_train, y_train)))\n",
    "print(\"testing score: {:.2f}\".format(model.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "f9c37d9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.92      0.94      2719\n",
      "           1       0.93      0.97      0.95      2756\n",
      "\n",
      "    accuracy                           0.95      5475\n",
      "   macro avg       0.95      0.95      0.95      5475\n",
      "weighted avg       0.95      0.95      0.95      5475\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, classification_report,accuracy_score,f1_score\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "cfm = confusion_matrix(y_test, pred)\n",
    "f1 = f1_score(pred, y_test)\n",
    "sns.heatmap(cfm, annot=True, fmt=\".0f\", linewidths=.5, square = True, cmap = 'Blues_r');\n",
    "plt.ylabel('Actual label');\n",
    "plt.xlabel('Predicted label');\n",
    "all_sample_title = 'F1 Score: {0}'.format(f1)\n",
    "plt.title(all_sample_title, size = 15);\n",
    "\n",
    "print(classification_report(y_test, pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "950dbb7d",
   "metadata": {},
   "source": [
    "## Result:\n",
    "\n",
    "1. On regression: random forest has mean abs error of 0.14, and lasso model output error 0.31, when y value with log transform, it redcue 0.23.\n",
    "\n",
    "2. On Classfication probelm, logistic output 0.93 f1 score on testset, and random forest output 0.99 f1 score on testset.\n",
    "3. Feature importance: total volume, large bags…"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9494bb2c",
   "metadata": {},
   "source": [
    "## Conclusion:\n",
    "1. Take aways: EDA is very important for this kind of business data, for example sales trends, sales different on different types can help understanding data.\n",
    "    \n",
    "2. Something that didnot work: simple linear regression perform poorly in this data.\n",
    "    \n",
    "3. Improvement: Observe outlier, remove them and analysis again."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13fcdb8b",
   "metadata": {},
   "source": [
    "Reference:\n",
    "\n",
    "https://www.kaggle.com/code/jaimebecerraguerrero/statistical-avo-eda-analysis-and-ml\n",
    "\n",
    "https://www.kaggle.com/code/ladylittlebee/linreg-knn-svr-decisiontreerandomforest-timeseries\n",
    "\n",
    "https://www.kaggle.com/code/samuelbelko/predicting-prices-of-avocados"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
